发表机构
University of Oxford（牛津大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究发现，大语言模型对不同语言提问的乌克兰战争评估存在亲俄或亲乌倾向差异，且该差异与全球政治分歧模式一致，提示信息战可能通过训练文本传播地缘政治偏见。
AI 中文摘要
人们越来越依赖AI聊天机器人获取新闻和世界事件的解释。但他们在使用不同语言提问时，是否会得到相同的政治答案？在此，我们表明，提问的语言可以改变同一AI系统对乌克兰战争的评估方式。我们让GPT、Claude和Gemini用112种语言评估关于该战争的二十条陈述，收集了67,200条回复。亲俄与亲乌回复之间的平衡因语言而异。当我们按国家的官方语言对回复进行分组时，它们呈现出类似于全球政治分歧的模式：相对更多亲俄的回答对应着公众对俄罗斯更友好的看法、在联合国投票中对乌克兰的支持较少，以及对乌克兰的援助较少。这一广泛模式在所有三个模型中重复出现，并且在移除个别陈述对后依然存在。我们的发现揭示了信息战可能影响用于训练AI模型的文本的一条潜在途径，而这又可能传播地缘政治偏见。
英文摘要
People increasingly turn to AI chatbots for news and explanations of world events. But do they receive the same political answers when they ask in different languages? Here we show that the language of a question can change how the same AI systems assess the war in Ukraine. We ask GPT, Claude and Gemini to evaluate twenty statements about the war in 112 languages, collecting 67,200 responses. The balance between Russia-leaning and Ukraine-leaning responses differs across languages. When we group responses by countries' official languages, they follow a pattern resembling worldwide political divisions: relatively more Russia-leaning answers correspond to more favourable public views of Russia, less support for Ukraine in United Nations votes, and less aid to Ukraine. The broad pattern recurs across all three models and remains when individual statement pairs are removed. Our findings suggest a possible route through which information warfare may shape the text used to train AI models, which may in turn spread geopolitical biases.
